Abstract

Session Overview
Kubernetes offers many ways to share GPUs, but a single, cluster-wide scheduler often forces trade-offs between utilization, stability, and team autonomy. This talk shows how vCluster makes the NVIDIA Kubernetes AI Scheduler (KAI) run as an opt-in service for each tenant—so platform teams can raise GPU density while keeping operations predictable.

What We’ll Cover

• Problem statement – why mixed workloads leave GPUs under-used and complicate on-call
• vCluster fundamentals – lightweight control planes that isolate scheduling logic, not hardware
• KAI at a glance – fractional GPU allocation, gang queues, topology awareness
• Live demonstration – two vClusters on one host

Key Takeaways

• A reproducible pattern for running different schedulers side-by-side
• Practical steps to increase GPU utilisation without adding more clusters
• An isolation model that lets teams experiment safely

Why It Matters
As GPU demand grows, platform engineers must balance cost efficiency with reliability. Combining vCluster and KAI delivers both—turning idle accelerators into productive capacity while preserving operational control.
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Bio

An active contributor to OpenSource projects on GitHub, blogger and content creator, focusing on practical, scalable solutions in cloud-native environments. DevOps and Platform Engineering practitioner and advocate. Visit: cloudrumble.net